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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク firefly algorithm ： ウィキペディア英語版 firefly algorithm The firefly algorithm (FA) is a metaheuristic algorithm, inspired by the flashing behaviour of fireflies. The primary purpose for a firefly's flash is to act as a signal system to attract other fireflies. Xin-She Yang formulated this firefly algorithm by assuming: #All fireflies are unisexual, so that any individual firefly will be attracted to all other fireflies; #Attractiveness is proportional to their brightness, and for any two fireflies, the less bright one will be attracted by (and thus move towards) the brighter one; however, the intensity (apparent brightness) decrease as their mutual distance increases; #If there are no fireflies brighter than a given firefly, it will move randomly. The brightness should be associated with the objective function. Firefly algorithm is a nature-inspired metaheuristic optimization algorithm. == Algorithm description == The pseudo code can be summarized as: Begin 1) Objective function: $f(\backslash mathbf),\; \backslash quad\; \backslash mathbf=(x\_1,x\_2,...,x\_d)$; 2) Generate an initial population of fireflies $\backslash mathbf\_i\; \backslash quad\; (i=1,2,\backslash dots,n)$;. 3) Formulate light intensity $I$ so that it is associated with $f(\backslash mathbf)$ (for example, for maximization problems, $I\; \backslash propto\; f(\backslash mathbf)$ or simply $I=f(\backslash mathbf)$; 4) Define absorption coefficient $\backslash gamma$ While (t < MaxGeneration) for i = 1 : n (all n fireflies) for j = 1 : n (n fireflies) if ($I\_j>I\_i$), move firefly i towards j; Vary attractiveness with distance r via $\backslash exp(-\backslash gamma\; \backslash ;\; r)$; Evaluate new solutions and update light intensity; end if end for j end for i Rank fireflies and find the current best; end while Post-processing the results and visualization; end The main update formula for any pair of two fireflies $\backslash mathbf\_i$ and $\backslash mathbf\_j$ is :: $\backslash mathbf\_i^=\backslash mathbf\_i^t\; +\; \backslash beta\; \backslash exp(r\_^2)\; (\backslash mathbf\_j^t\; -\; \backslash mathbf\_i^t)\; +\backslash alpha\_t\; \backslash boldsymbol\_t$ where $\backslash alpha\_t$ is a parameter controlling the step size, while $\backslash boldsymbol\_t$ is a vector drawn from a Gaussian or other distribution. It can be shown that the limiting case $\backslash gamma\; \backslash rightarrow\; 0$ corresponds to the standard Particle Swarm Optimization (PSO). In fact, if the inner loop (for j) is removed and the brightness $I\_j$ is replaced by the current global best $g^$ *, then FA essentially becomes the standard PSO. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「firefly algorithm」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.